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By introducing a small set of additional parameters, a probe learns to solvespecific linguistic tasks (e.g., dependency parsing) in a supervised mannerusing feature representations (e.g., contextualized embeddings). Theeffectiveness of such probing tasks is taken as evidence that the pre-trainedmodel encodes linguistic knowledge. However, this approach of evaluating alanguage model is undermined by the uncertainty of the amount of knowledge thatis learned by the probe itself. Complementary to those works, we propose aparameter-free probing technique for analyzing pre-trained language models(e.g., BERT). Our method does not require direct supervision from the probingtasks, nor do we introduce additional parameters to the probing process. Ourexperiments on BERT show that syntactic trees recovered from BERT using ourmethod are significantly better than linguistically-uninformed baselines. Wefurther feed the empirically induced dependency structures into a downstreamsentiment classification task and find its improvement compatible with or evensuperior to a human-designed dependency schema.

Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT